This book uses machine-learning to identify the causes of conflict from among the top predictors of conflict. This methodology elevates some complex causal pathways that cause civil conflict over others, thus teasing out the complex interrelationships between the most important variables that cause civil conflict. Success in this realm will lead to scientific theories of conflict that will be useful in preventing and ending civil conflict. After setting out a current review of the literature and a case for using machine learning to analyze and predict civil conflict, the authors lay out the data set, important variables, and investigative strategy of their methodology. The authors then investigate institutional causes, economic causes, and sociological causes for civil conflict, and how that feeds into their model. The methodology provides an identifiable pathway for specifying causal models. This book will be of interest to scholars in the areas of economics, political science, sociology, and artificial intelligence who want to learn more about leveraging machine learning technologies to solve problems and who are invested in preventing civil conflict.

About the Author

Atin Basuchoudhary is the Roberts Professor of Free Enterprise Economics in the Department of Economics and Business at the Virginia Military Institute, USA.

James T. Bang is the Economics Chair and Professor in the Department of Economics at St. Ambrose University, USA.

John David is Professor of Applied Mathematics in the Department of Applied Mathematics, Jackson-Hope Distinguished Professor of Natural Science, and the Director Applied and Industrial Mathematics Program at the Virginia Military Institute, USA.

Tinni Sen is the Alexander P. Morrison 1939 Professor of Economics and Business in the Department of Economics and Business at the Virginia Military Institute, USA.

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